Phase angle and its determinants in healthy subjects: influence of body composition

ABSTRACT

Background: The phase angle (PA) has been used as a prognostic marker in several clinical situations. Nevertheless, its biological meaning is not completely understood.

Objective: We verified how body-composition components could explain the PA.

Design: The trial was a cross-sectional study involving 1442 participants (women: 58.5%; Caucasian: 40.2%) from body-composition studies. Labeled tritium dilution and total-body potassium were used to estimate total-body water (TBW) and intracellular water (ICW), respectively. Extracellular water (ECW) and the ECW:ICW ratio were estimated from the difference and the ratio of these values. Fat-free mass (FFM) and fat mass (FM) were estimated with the use of dual-energy X-ray absorptiometry, underwater weighing (UWW), and TBW. The PA was estimated with the use of a single-frequency bioelectrical impedance analysis system. Correlations between the PA and all body-composition variables were evaluated. A multivariate linear regression analysis was performed to adjust for the effects of body-composition variables on the PA variability. All analyses were performed separately by sex.

Results: Compared with men, women exhibited significantly larger ECW:ICW ratios and FM. The highest positive correlation was shown between the PA and FFM obtained with the use of UWW (both sexes). The highest negative correlation was shown between the PA and ECW:ICW ratios for both sexes. Age, race, height, ECW:ICW, and FFM from UWW were significant PA determinants in a multivariate linear regression model. Even after adjustment for all significant covariates, the explained PA variance was low (adjusted R² = 0.539 and 0.421 in men and women, respectively). The greatest impact on the total PA prediction in both men and women were age, FFM, and height.

Conclusions: Age is the most significant PA predictor in men and women followed by FFM and height. The ECW:ICW contribution may explain the association of the PA observed in the clinical setting and in people who are obese.

INTRODUCTION

A bioelectrical impedance analysis (BIA)⁶ has been widely used over the past 30 y as a method of measuring body composition in clinical practice. The increasing use of a BIA is due to the advantages associated with this method, including the low cost, instrument portability, and ease of measurement compared with the use of other instruments that are often limited to research environments. However, despite extensive literature showing the usefulness of a BIA in body-composition assessment in groups of healthy subjects, there remain limitations when evaluating individual subjects in the clinical setting (1).

In addition to measuring resistance and reactance, a BIA can also provide an estimate of the phase angle (PA). Although BIA predictions of body composition often rely on population-specific equations, the PA is estimated directly without additional conversion to specific body compartments. The PA concept is based on changes in resistance and reactance as alternating current passes through evaluated tissues. A phase shift occurs as part of the current is stored in the resistive compartments of cellular membranes. Therefore, the measured PA depends on several biological factors such as the quantity of cells with their respective cell membranes, cell membrane integrity, and related permeability and the amounts of extracellular and intracellular fluids (1). A PA theory and applications in clinical practice have been discussed in several previous reviews (1–3). Nevertheless, the electrical theory of PA as it relates to in vivo biological effects remains incomplete.

At an empirical level, some studies have reported that age, sex, and BMI are major PA determinants in healthy subjects (2). Reference values for the PA vary indifferent populations although all populations share similar features in relation to age and sex (4). Although Bosy-Westphal et al. (5) reported an association between BMI and the PA in a healthy German population, to our knowledge, no other studies have directly assessed the influence of body-composition components on measured values for the PA. The PA has also been used as a nutritional status marker because the measured values reflect the amounts of various tissue compartments and hydration status (1).

Malnutrition and the presence of inflammation also appear to influence the measured PA (6) with low values that are typically related to more-severe illnesses and worse clinical prognoses. Applications in patients with acute and chronic diseases require reference values for the PA. The aims of the current study were to evaluate the relation between the PA and body composition and how much the PA variability could be explained by body composition in a large sample of healthy adults.

METHODS

The subject pool included data from our previously published study (4). These data included healthy subjects who were evaluated between 1986 and 1999 at St. Luke’s–Roosevelt Hospital Center. The volunteers were recruited from the hospital staff and the local community. All participants signed a written consent form, and the Institutional Review Board of St. Luke’s–Roosevelt Hospital approved the protocols.

Body composition was estimated with the use of several methods that were described in detail in previous publications (4, 7) and are summarized in the following text.

All subjects were assessed after a ≥8-h fast. Body weight was measured to the nearest 0.1 kg with the use of a Weight-Tronix scale (Scale Electronics Development). Height was measured to the nearest 0.1 cm with the use of a wall-mounted stadiometer (Holtain Ltd.). BMI (in kg/m²) was calculated as body weight divided by the square of height. Total body water (TBW; in L) was estimated with the use of labeled tritium dilution (³H₂O). Intracellular water (ICW) was quantified with the use of total-body potassium with extracellular water (ECW) estimated as the difference between TBW and ICW (8). The fluid distribution was calculated as the ECW:ICW ratio. Fat-mass (FM) and fat-free mass (FFM) compartments were estimated with the use of underwater weighing (UWW), dual energy X-ray absorptiometry (Lunar DPX software 3.6; GE Lunar), and TBW. BIA measurements were made after a 5-min rest in a supine position with RJL model 101 analyzer (RJL Systems), which uses an 800-μA current at a single frequency of 50 KHz. The PA was estimated as

Statistical analyzes were conducted with STATA 12.1 (STATA Corp.). All analyzes were performed separately for each sex group. Descriptive tables present the results as the median and IQR of continuous variables. Sex differences between body-composition variables were tested with the use of the Mann-Whitney U test. We evaluated the correlations between the PA and all other measures (i.e., FM, FFM, and ECW:ICW). A multivariate linear regression analysis was performed to adjust the effect of multiple variables and to identify significant determinants of the PA. The η² estimation was used to estimate the proportion of the total variance associated with each variable after the linear regression analysis. P < 0.05 was considered statistically significant.

RESULTS

The initial sample included 1967 subjects, and only those individuals with complete body-composition evaluations (n = 1442) were included in the current report. More than one-half of the subjects (58.5%) were women. The sample was composed of Caucasians (n = 579; 40.2%), African Americans (n = 387; 26.8%), Asians (n = 143; 9.9%), and Hispanics (n = 86; 6.0%). The remainder of the sample was identified as other or multiracial. The median sample age was 43 y (IQR: 31–61).

The sample body-composition characteristics are summarized in Table 1. All measures except BMI were significantly different (P < 0.001) between men and women. The men had greater heights and larger TBW, FFM, and PA values (all P < 0.001). The women had larger ECW:ICW ratios and all FM measures than those of the men (all P < 0.001).

TABLE 1.

Body-composition characteristics of the 1442 healthy subjects¹

	Men (n = 599)	Women (n = 843)	P
Weight, kg	76.1 (68.2–86.1)	65.7 (56.8–79.1)	<0.001
Height, cm	173.8 (169.0–179.0)	161.5 (157.0–166.2)	<0.001
BMI, kg/m2	25.3 (22.9–28.2)	25.6 (22.0–30.0)	0.4
TBW, kg	44.6 (39.6–49.2)	32.2 (28.9–35.6)	<0.001
ECW:ICW	0.79 (0.70–0.91)	1.03 (0.91–1.16)	<0.001
FM-DXA, kg	16.1 (10.8–23.4)	24.5 (16.6–34.8)	<0.001
FM-UWW, kg	17.6 (11.6–23.7)	23.7 (16.6–33.0)	<0.001
FM-TBW, kg	15.5 (10.2–22.0)	22.8 (15.9–32.1)	<0.001
FFM-DXA, kg	60.4 (53.9–66.0)	42.5 (38.8–46.3)	<0.001
FFM-UWW, kg	59.6 (53.2–65.5)	43.4 (38.6–48.1)	<0.001
FFM-TBW, kg	61.2 (54.2–67.3)	44.1 (39.6–48.8)	<0.001
PA, o	7.58 (6.79–8.20)	6.51 (5.89–7.11)	<0.001

¹All values are medians; IQRs in parentheses. P values were determined with the use of the Mann-Whitney U test. ECW:ICW, extracellular water:intracellular water ratio; FFM-DXA, fat-free mass from dual-energy X-ray absorptiometry; FFM-TBW, fat-free mass from total body water; FFM-UWW, fat-free mass from underwater weighing; FM-DXA, fat mass from dual-energy X-ray absorptiometry; FM-TBW, fat mass from total body water; FM-UWW, fat mass from underwater weighing; PA, phase angle; TBW, total-body water.

Pearson correlation values between the PA and the other anthropometric and body-composition variables are presented in Table 2. All correlations were significant. The highest positive correlation was shown between the PA and FFM obtained with the use of UWW (r = 0.43 and 0.45 for men and women, respectively). The highest negative correlation was shown between the PA and ECW:ICW (r = −0.39 and −0.20 for men and women, respectively). A negative correlation between the PA and FM was observed in men (r varied from −0.10 to −0.18 according to the method), whereas this correlation was positive in women (r varied from 0.17 to 0.23).

TABLE 2.

Pearson correlation coefficients (r) between the phase angle and all other anthropometric and body-composition variables in the 1442 healthy subjects¹

	Men	Women
Weight	0.16	0.31
Height	0.12	0.11
BMI	0.12	0.28
TBW	0.39	0.40
ECW:ICW	−0.39	−0.20
FM-DXA	−0.10	0.23
FM-UWW	−0.18	0.17
FM-TBW	−0.16	0.20
FFM-DXA	0.39	0.38
FFM-UWW	0.43	0.45
FFM-TBW	0.39	0.40

¹All correlations were significant at P < 0.05. ECW:ICW, extracellular water:intracellular water ratio; FFM-DXA, fat-free mass from dual-energy X-ray absorptiometry; FFM-TBW, fat-free mass from total body water; FFM-UWW, fat-free mass from underwater weighing; FM-DXA, fat mass from dual-energy X-ray absorptiometry; FM-TBW, fat mass from total body water; FM-UWW, fat mass from underwater weighing; TBW, total-body water.

Multivariate linear regression analyses were performed separately for men and women. The following variables were tested in these analyses: age, weight, height, BMI, race, TBW, ECW:ICW, and FFM obtained with the use of UWW, which is the method that exhibited the best correlation with the PA. Table 3 presents the results for this analysis for the men and women.

TABLE 3.

Significant phase-angle determinants as observed with the use of multivariate linear regression modeling¹

	Men (n = 599)2			Women (n = 843)3
	β	η2	P	β	η2	P
Age, y	−0.029	13.45	<0.001	−0.020	10.48	<0.001
Race4
African American	0.276	0.84	0.001	0.315	1.79	<0.001
Hispanic	0.072	0.02	0.601	0.210	0.25	0.06
Asian	−0.090	0.05	0.425	−0.379	1.17	<0.001
Other	0.044	0.02	0.621	0.002	0.001	0.98
Height, cm	−0.059	6.29	<0.001	−0.039	5.41	<0.001
BMI, kg/m2	−0.024	0.39	0.025	—	—	—
ECW:ICW	−0.847	1.15	<0.001	−0.826	2.38	<0.001
FFM-UWW, kg	0.062	6.68	<0.001	0.048	8.14	<0.001

¹ECW:ICW, extracellular water:intracellular water ratio; FFM-UWW, fat-free mass obtained from underwater weighing.

²Adjusted R² = 0.539.

³Adjusted R² = 0.421.

⁴Caucasians were considered the reference race.

Age, race, height, ECW:ICW, and FFM were associated with the PA in men and women. By contrast, BMI was associated with PA only in men. In addition to race, the only variable that was positively associated in men was FFM. Greater age, height, BMI, and ECW:ICW were associated with a lower PA, and the largest negative β value was for the ECW:ICW ratio (β = −0.847). African American men had PA values that were 0.276° higher than those of Caucasian men although all other characteristics were similar.

In women, BMI was not statistically associated with the PA. The ECW:ICW ratio, height, and age also had negative β values and, thus, a negative relation with the PA. African American women had, on average, PA values that were 0.315° larger than those of Caucasian women, whereas Asian women had, on average, a 0.379° lower PA than that of Caucasian women.

After all of the previously noted evaluated variables were controlled for, one-half of the PA variability was explained in men (adjusted R² = 0.539), and the explained variance was even less in women (adjusted R² = 0.421). Table 3 also presents η² values that explain how much (the percentage) each variable contributed to the total variability explained by the final model. Age explained the highest variability on the PA in both men and women followed by FFM and height. Figure 1 presents, in a graph, the proportional contribution of each variable to the total variance of this model that was obtained from the multivariate linear regression analysis. As shown, >0% of the variance of the model for both sexes was explained by the 3 variables previously mentioned.

FIGURE 1.

Total variance proportion (R² proportion) observed for each variable with the use of a multiple linear regression analysis in phase-angle prediction models for men and women according to η² values. For men, age, race, height, the ECW:ICW, FFM-UWW, and BMI together accounted for <55% of the phase-angle variance (R²-men = 0.546). For women, age, race, height, the ECW:ICW, and FFM-UWW together explained <43% of the phase-angle variance (R²-men = 0.426). Afr Am, African Americans; ECW:ICW, extracellular water:intracellular water ratio; FFM-UWW, fat-free mass obtained from underwater weighing.

DISCUSSION

Although its biological meaning is not completely understood, the PA has been used extensively as an important prognostic marker in several clinical situations. In a recently published review, several studies that used the PA as a prognostic marker in cancer, HIV, dialysis, and other clinical situations associated smaller PA values with a poor prognosis or a shorter survival time (2). Thus, the PA has been considered to be a general health or nutritional status marker (9, 10). Nevertheless, to our knowledge, no previous study has investigated PA determinants in healthy subjects with the use of body-composition components obtained from methods that are considered reference methods. With this rationale, our study provided us the opportunity to assess directly the association between the PA and associated factors.

The importance of age in PA variation has been shown previously in other population-based studies in which a trend of a decreasing PA could be seen in both men and women (4, 5). During clinical situations, age also appeared to be an important determinant of the PA even in the presence of an infection or malnutrition (6). This study showed that age is the most important biological determinant of PA variation. Aging, by itself, is associated with other determinant PA variables such as a smaller body cell mass followed by a compensatory increase in the extracellular volume leading to a higher ECW:ICW ratio (8).

An interesting finding was the contribution of race as a significant determinant of the PA. This finding confirmed that it is not possible to use universal reference values for the PA. Other studies have suggested that different populations should have their own reference PA values although the different populations showed the same trend of variation according to sex and age (4, 5). This finding reinforces the idea that each population should use its specific risk cutoffs that are based on its specific reference PA values with the use of a standardized PA. This approach should be preferred to the use of unique fixed cutoff values for each disease as several authors have suggested (11–13).

Stature was a PA determinant with a negative β value even after adjustment for all other variables. This observation implies that a PA is smaller in magnitude with greater height. Note that even in the presence of height as a covariate, BMI was also a significant PA determinant in men. To our knowledge, no previous study has related this biological variable as a PA determinant. One hypothesis is that the importance of height is related to the FFM component, suggesting that similar amounts of FFM could determine the variation in PA according to the subject’s height. This idea is similar to the concept of a fat-free mass index (FFMI), which is an important prognostic factor that is based on body composition. A recent published study from our group showed that a low FFMI was associated with a greater mortality rate in cancer patients regardless of BMI or FM (14).

The ECW:ICW ratio has been examined previously and shown to be one determinant of PA variation. The authors related PA variability to alterations in the cell size, the permeability of the cell membrane, or differences in the distribution of fluids in the tissues (15). Our results confirm that the ECW:ICW ratio is a significant determinant of the PA. The alterations in the PA associated with disease, malnutrition, and physical inactivity could be justified by modifications in FFM that have been commonly shown in these clinical scenarios. However, even in situations when FFM loss has not occurred, extracellular fluid expansion can lead to an increase in the ECW:ICW ratio. This outcome could occur in sepsis and the early phases of malnutrition leading to a decrease in the PA. Therefore, a periodic assessment of the PA in severely ill patients could be an auxiliary tool in the early diagnosis of these clinical conditions.

Because of the limitation of the use of a 2-compartment model, FM and FFM obtained by the same methods could not be analyzed simultaneously in the regression models. However, the lower PA shown in obese subjects with BMI > 35, as shown by Bosy-Westphal et al. (5), could be explained by other variables included in our model. Dittmar (16) suggested that obese subjects have greater hydration and fluids in the extracellular compartments, which could lead to a larger ECW:ICW ratio. This increased ECW:ICW ratio could be the reason for the lower PA in the overweight and obese subjects. Mazariegos et al. (17) showed that severely obese patients in the preoperative period of bariatric surgery had an increased ECW:ICW ratio compared with that of a control group. Moreover, this alteration did not reverse even after surgery. Thus, the excessive adipose tissue influences, in a definitive way, the hemodynamic or fluid, thereby maintaining these abnormalities.

Even after the inclusion of all body-composition variables, only slightly >50% of PA variability could be explained in the men, and even a smaller amount could be explained in the women. Some studies have shown that smaller PA values are also associated with a decrease in function as assessed by handgrip strength (18, 19). Some recently published studies have suggested that the PA might be inversely related not only to muscle mass but also to strength in elderly subjects (20). Therefore, the PA could be an auxiliary tool in the diagnosis of sarcopenia. The inclusion of functional variables in the analysis could further increase our knowledge of PA variability in healthy subjects.

In conclusion, we showed that age and the combination of FFM and height were the most important variables that explain PA variability in healthy subjects. The ECW:ICW ratio may justify the variations shown in PA in several clinical situations and severe obesity. Race has only a small relation with PA variation, but this relation justifies the need for specific reference values for each population. Future studies that use functional variables could further increase our knowledge of the biological importance of the PA in healthy and diseased patients.

ABBREVIATIONS

BIA

bioelectrical impedance analysis

ECW

extracellular water

FFM

fat-free mass

FM

fat mass

ICW

intracellular water

PA

phase angle

TBW

total-body water

UWW

underwater weighing.